Towards Self-evolving Context-aware Services

The introduction of new communication infrastructures such as Beyond 3rd Generation (B3G) and the widespread usage of small computing devices are rapidly changing the way we use and interact with technology to perform everyday tasks. Ubiquitous networking empowered by B3G networking makes it possible for mobile users to access networked software services across continuously changing heterogeneous infrastructures by resource-constrained devices. Heterogeneity and devices' limitedness, create serious problems for the development and dynamic deployment of mobile applications that are able to run properly on the execution context and consume services matching with the users' expectations. Furthermore, the everchanging B3G environment calls for applications that self-evolve according to context changes. Out of these problems, self-evolving adaptable applications are increasingly emerging in the software community. In this paper we describe how CHAMELEON, a declarative framework for tailoring adaptable applications, is being used for tackling adaptation and self-evolution within the IST PLASTIC project

Travel Time Prediction and Explanation with Spatio-Temporal Features: A Comparative Study

Travel time information is used as input or auxiliary data for tasks such as dynamic navigation, infrastructure planning, congestion control, and accident detection. Various data-driven Travel Time Prediction (TTP) methods have been proposed in recent years. One of the most challenging tasks in TTP is developing and selecting the most appropriate prediction algorithm. The existing studies that empirically compare different TTP models only use a few models with specific features. Moreover, there is a lack of research on explaining TTPs made by black-box models. Such explanations can help to tune and apply TTP methods successfully. To fill these gaps in the current TTP literature, using three data sets, we compare three types of TTP methods (ensemble tree-based learning, deep neural networks, and hybrid models) and ten different prediction algorithms overall. Furthermore, we apply XAI (Explainable Artificial Intelligence) methods (SHAP and LIME) to understand and interpret models’ predictions. The prediction accuracy and reliability for all models are evaluated and compared. We observed that the ensemble learning methods, i.e., XGBoost and LightGBM, are the best performing models over the three data sets, and XAI methods can adequately explain how various spatial and temporal features influence travel time